Abstract
This paper presents a differential-evolution- (DE-) optimized, independent multiloop proportional-integral-derivative (PID) controller design for full-car nonlinear, electrohydraulic suspension systems. The multiloop PID control stabilises the actuator via force feedback and also improves the system performance. Controller gains are computed using manual tuning and through DE optimization to minimise a performance index, which addresses suspension travel, road holding, vehicle handling, ride comfort, and power consumption constraints. Simulation results showed superior performance of the DE-optimized PID-controlled active vehicle suspension system (AVSS) over the manually tuned PID-controlled AVSS and the passive vehicle suspension system (PVSS).
Highlights
The evolution of modern instrumentation and control techniques has made semi-active vehicle suspension systems (SAVSS) [1] and AVSS designs more promising
There is a vast amount of literature documented about AVSS design, most are affected by at least one of the following limitations: use of linear suspension models, ignoring actuator dynamics and performance evaluation based on the 2 degree-of-freedom quarter-car model [2, 3]
Linear optimal control schemes like linear quadratic regulator (LQR), linear quadratic gaussian (LQG), H∞, mixed H2/H∞, and linear parameter varying (LPV) control methods are well-developed control schemes that have been employed in SAVSS and AVSS designs [1, 4,5,6,7,8]
Summary
The evolution of modern instrumentation and control techniques has made semi-active vehicle suspension systems (SAVSS) [1] and AVSS designs more promising. Linear optimal control schemes like linear quadratic regulator (LQR), linear quadratic gaussian (LQG), H∞, mixed H2/H∞, and linear parameter varying (LPV) control methods are well-developed control schemes that have been employed in SAVSS and AVSS designs [1, 4,5,6,7,8] Their stability and robustness properties are more readily established but always limited when employed for complex nonlinear control schemes; they normally assume time-invariant situation. The global optimization algorithm is inherently flexible and relatively simpler in comparison with other techniques It gives better search space exploration characteristics with similar or even better results than previously employed optimization routines [26, 29, 30]. The objective function simultaneously addressed the conflicting trade-off challenge in the AVSS design
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